Informing policy on school and daycare operations during COVID-19 with a living rapid evidence review
Bibliographic record
Abstract
Abstract Background To support evidence-informed decision making (EIDM) for safe re-opening and operation of schools and daycares, the National Collaborating Centre for Methods and Tools (NCCMT) has maintained since May 2020 a living rapid review answering the following question: “What is the role of schools and daycares in COVID-19 transmission”. Traditional rapid review methodology was modified for the COVID-19 context. This presentation will describe the global reach and usefulness of this living rapid review. Methods Following completion of each update of the living rapid review, findings were disseminated broadly with the aim of informing policy and public health practice. Key dissemination strategies include e-mails to key contacts and a subscriber list; highlight in a monthly newsletter; media outreach; and social media. The review's reach was analyzed using Google Analytics, citation tracking, and qualitative feedback. Results Between May 2020 and April 2021, the living review has been updated 14 times. The posted review has been viewed over 5000 times across 46 countries. The review has been cited and indexed in over 40 sources, including key governmental and non-governmental reports and guidelines. The NCCMT has received positive qualitative feedback on the review's value in informing the public health response related to schools and daycares in various jurisdictions across Canada. Key stakeholders have expanded the review's reach organically as they use the evidence in practice and share the review with their networks. Lessons Using a living rapid review to continuously provide high-quality synthesized evidence amidst the evolving COVID-19 research literature demonstrates a responsive approach to decision makers' requests for evidence. An emerging challenge is reaching the proper stakeholders responsible for EIDM, particularly during public health emergencies with many competing high-priority questions and decisions to be made. Key messages As the evidence landscape changes due to a surge in literature, evidence-informed decision making can be supported by rapid but rigorous syntheses that evaluate quality and emerging recommendations. A long-standing, trusting relationship with decision makers is key to optimizing living rapid review methodology to meet the evidence needs of decision makers despite the changing literature.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.396 | 0.673 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.028 | 0.022 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.023 | 0.017 |
| Open science | 0.007 | 0.013 |
| Research integrity | 0.009 | 0.010 |
| Insufficient payload (model declined to judge) | 0.013 | 0.004 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".